Cells have a number of well-established uses in molecular biology. For example, cells are commonly used as hosts for manipulating DNA in processes such as transformation and recombination. Cells are also used for expression of recombinant proteins encoded by DNA transformed/transfected or otherwise introduced into the cells. Some types of cells are also used as progenitors for generation of transgenic animals and plants. Although all of these processes are now routine, in general, the genomes of the cells used in these processes have evolved little from the genomes of natural cells, and particularly not toward acquisition of new or improved properties for use in the above processes.
The traditional approach to artificial or forced molecular evolution focuses on optimization of individual genes having discrete and selectable phenotypes. The strategy is to clone a gene, identify a discrete function for the gene and an assay by which it can be selected, mutate selected positions in the gene (e.g., by error-prone PCR or cassette mutagenesis) and select variants of the gene for improvement in the known function of the gene. A variant having improved function can then be expressed in a desired cell type. This approach has a number of limitations. First, it is only applicable to genes that have been isolated and functionally characterized. Second, the approach is usually only applicable to genes that have a discrete function. In other words, multiple genes that cooperatively confer a single phenotype cannot usually be optimized in this manner—and many genes have cooperative functions. Finally, this approach can only explore a very limited number of the total number of permutations even for a single gene and even fewer permutations when complete genomes are considered. For example, varying even ten positions in a protein with every possible amino acid would generate 2010 variants, which is more than can be accommodated by existing methods of transfection and screening.
In view of these limitations, traditional approaches are inadequate for improving cellular genomes in many useful properties. For example, to improve a cell's capacity to express a recombinant protein might require modification in any or all of a substantial number of genes, known and unknown, having roles in transcription, translation, posttranslational modification, secretion or proteolytic degradation, among others. Attempting individually to optimize even all the known genes having such functions would be a virtually impossible task, let alone optimizing hitherto unknown genes which may contribute to expression in manners not yet understood.
For example, one area where traditional methods are used extensively is in the fermentation industry. The primary goal of current strain improvement programs (SIPs) in fermentation is typically an increase in product titre. State-of-the-art mutagenesis and screening is practiced by large fermentation companies, such as those in the pharmaceutical and chemical industries. Parent strains are mutated and individual fermentations of 5,000-40,000 mutants are screened by high-throughput methods for increases in product titre. For a well developed strain, an increase in yield of 10% per year (i.e., one new parent strain per year) is achieved using these methods. In general, cells are screened for titre increases significantly above that of the parent, with the detection sensitivity of most screens being ˜5% increase due to variation in growth conditions. Only those that “breed true” during scale up make it to production and become the single parent of the next round of random mutagenesis.
Employing optimal mutation conditions, one mutant out of 5,000-40,000 typically has a titre increase of 10%. However, a much higher percentage has slightly lower titre increases, e.g., about 4-6%. These are generally not pursued, since experience has demonstrated that a higher producer can be isolated and that a significant percent of the lower producers actually are no better than the parent strain (i.e., the variance observed is due to experimental artifact, rather than actual differences). The key to finding high producers using current strategies is to screen very large numbers of mutants per round of mutagenesis and to have a stable and sensitive assay. For these reasons, R&D to advance this field are in the automation and the screening capacity of the SIPs. Unfortunately, this strategy is inherently limited by the value of single mutations to strain improvement and the growth rate of the target organisms.
The present invention overcomes the problems noted above, providing, inter alia, novel methods for evolving the genome of whole cells and organisms.